most citedReliable deep-learning-based phase imaging with uncertainty quantification

206 citations · 212 across the 3 of their papers we have counts for

collaborators

6 papers

physics.med-ph2020

Diffuser-based computational imaging funduscope

Yunzhe Li, Gregory N. McKay, Nicholas J. Durr +1

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potenti…

q-bio.QM20193 cited

Inverse scattering for reflection intensity phase microscopy

Alex Matlock, Anne Sentenac, Patrick C. Chaumet +2

Reflection phase imaging provides label-free, high-resolution characterization of biological samples, typically using interferometric-based techniques. Here, we investigate reflect…

eess.IV2019

SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors

Zihui Wu, Yu Sun, Alex Matlock +3

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large d…

physics.optics2019

High-speed in vitro intensity diffraction tomography

Jiaji Li, Alex Matlock, Yunzhe Li +3

We demonstrate a label-free, scan-free {\it intensity} diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume 3D refractive ind…

physics.optics20193 cited

Optimal illumination scheme for isotropic quantitative differential phase contrast microscopy

Yao Fan, Jiasong Sun, Qian Chen +3

Differential phase contrast microscopy (DPC) provides high-resolution quantitative phase distribution of thin transparent samples under multi-axis asymmetric illuminations. Typical…

eess.IV2019206 cited

Reliable deep-learning-based phase imaging with uncertainty quantification

Yujia Xue, Shiyi Cheng, Yunzhe Li +1

Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessmen…